Video Feature Fusion Across Receptive Fields for Accurate Querying
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Solution Overview
Problem
Existing video processing methods struggle to effectively combine local and global features for improved expression and accuracy in applications such as video classification and query, often relying on end-to-end CNN or Transformer-based approaches that do not fully leverage the benefits of both types of features.
Innovation Solution
A method involving a Video Swin-Transformer model that extracts local and global features through multiple receptive fields, merging them using a cross-attention mechanism to enhance the video's feature representation, thereby improving accuracy in video classification and query tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple receptive fields are used to extract video features, then the expression ability of features is improved, but the device complexity increases
Solution Approach 1:
The patent divides the feature extraction process into multiple parallel branches, each handling a different receptive field size. The video processing model is segmented into several feature extraction paths that operate independently and then merge their outputs, allowing comprehensive feature capture without overwhelming complexity in a single path.
Solution Approach 2:
The patent introduces the dimension of receptive field size as an additional variable in the feature extraction process. By varying the receptive field dimension across multiple branches and then merging them, the system captures features at multiple scales simultaneously, enhancing expression ability without linearly increasing complexity.
2Manufacturing precision
If local and global features are merged, then the accuracy of downstream applications is improved, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent introduces an attention mechanism as an intermediary component that selectively weights and integrates local and global features. This intermediary layer manages the complexity of merging features by dynamically determining which features are most relevant, simplifying the integration process while maintaining high accuracy.
Solution Approach 2:
The patent dynamically adjusts the weighting parameters of local and global features through the attention mechanism. By changing these parameters based on the input content, the system optimizes the fusion of features for different video scenarios, improving accuracy without requiring manual tuning of complex integration rules.
Data Source
AI summary
The present application provides a method of processing a video, a method of querying a video, and a method of training a video processing model. A specific implementation solution of the method of processing the video includes: extracting, for a video to be processed, a plurality of video features under a plurality of receptive fields; extracting a local feature of the video to be processed according to a video feature under a target receptive field in the plurality of receptive fields; obtaining a global feature of the video to be processed according to a video feature under a largest receptive field in the plurality of receptive fields; and merging the local feature and the global feature to obtain a target feature of the video to be processed.